LNEMLC: Label Network Embeddings for Multi-Label Classifiation

Abstract--Multi-label classification aims to classify instances with discrete non-exclusive labels. Most approaches on multilabel classificationfocus on effective adaptation or transformation of existing binary and multi-class learning approaches but fail in modelling the joint probability of labels or do not preserve generalization abilities for unseen label combinations. To address these issues we propose a new multi-label classification scheme, LNEMLC - Label Network Embedding for Multi-Label Classification, thatembeds the label network and uses it to extend input space in learning and inference of any base multi-label classifier. The approach allows capturing of labels' joint probability at low computational complexity providing results comparable to the best methods reported in the literature. We demonstrate how the method reveals statistically significant improvements over the simple kNN baseline classifier. We also provide hints for selecting the robust configuration that works satisfactory across data domains. I. INTRODUCTION In our daily life, we continuously encounter data classified with multiple categories. Be it youtube videos, Instagram photos, articles in newspapers or more recently even our genome on gene analysis websites; we depend heavily on labels to guide us through various types of objects to find that which is to our liking and we rely on labels to organize our information flow. Labels usually denote the simplest understandable terms, while it is from how they occur together that creates sophisticated concepts and contexts.